Did I choose the correct dependent and independent variables? I ran a regression analysis and don't exactly know how to interpret it.

1. Fuel demand in OECD countries: This largely replicates what you saw in the lectures. Download the Global Fuel o Restricting your data to the OECD subset for now, create two scatter Data 2010 & and read the plots showing the relationship between fuelcon and fuelprice and "Notes" tab to make sure that between fuelcon and gappc. Briefly describe what they imply about the you are familiar with all the relationships between these variables. variables. o Use multiple regression (for the OECD sample) to estimate the demand function for motor fuels (again using fuelcon and fuelprice), with quantity as a function of both the price and income (per capita GDP). Run the regression once in a linear specification and once in terms of the natural logs of the variables. Display the tables of results. o Discuss the regression results: interpret the slope coefficients, discuss their statistical significance, and discuss the goodness of fit of the regressions. Are the results consistent with what you would expect for a demand relationship? How do you interpret the slope coefficients in the regressions in logs? Are they of a magnitude you would expect?Fuel Price vs. Fuel Conscumption GDPPC vs. Fuel Consumption 600 600 500 500 . . 400 400 Fuel Consumption (consumption of gasoline plus diesel fuel in gallons per year per capita) konsumption of gasoline plus diesel fuel in gallons per year per capita] 300 Fuel Consumption 30 200 200 100 100 0 10 10100 20000 3:00 00 400 00 500 0D 600 00 700 00 90000 Fuel Price GDPPC [average of gasoline and diesel price in US$ per gallon, weighted by consumption shares) (GDP per capita in US$) FUEL PRICE VS. FUEL CONSUMPTION SUMMARY OUTPUT GDPPC VS. FUEL CONSCUMPTION SUMMARY OUTPUT Regression Statistics Regression Statistics Multiple R 0.05458233 Multiple R 0.37957975 R Square 0.00297923 R Square 0.14408079 Adjusted R Square -0.0056905 Adjusted R Square D.13663801 Standard Error 104.294934 Standard Error 17020.6886 Observations 117 Observations 117 ANOVA ANOVA 55 MS F Significance F of sS MS F Significance F Regression 1 3737.870553 3737.87055 0.34363535 0.558887132 Regression 1 5608224433 5608224433 19.3584746 2.43456E-05 Residual 115 1250904.813 10877.4332 Residual 115 33315941615 289703840 Total 116 1254642.684 Tota 116 38924166048 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 96.5415483 25.36825492 3.80560463 0.00022835 46.29191767 146.791179 46.2919177 146.791179 Intercept 1622.1482 4140.039718 -0.3918195 0.69591673 -9822.770111 6578.47368 -9822.7701 6578.47368 X Variable 1 2.77534786 4.734438815 0.58620419 0.55888713 -6.602664246 12.15336 -6.6026642 12.15336 X Variable 1 3399.52304 772.6493129 4.39982666 2.4346E-05 1869.053457 4929.99263 1869.05346 4929.99263